Detection of in-band interference

ABSTRACT

A method is provided. In some examples, the method includes performing, by processing circuitry, a first transform operation on a first time-domain data set to generate a frequency-domain data set. In addition, the method includes determining, by the processing circuitry, that at least one portion of the frequency-domain data set satisfies a first threshold magnitude. The method also includes performing, by the processing circuitry, an inverse transform operation on the at least one portion of the frequency-domain data set to generate a second time-domain data set. The method further includes identifying, by the processing circuitry and based on the second time-domain data set, a region of interference in the first time-domain data set.

This application claims the benefit of Indian Provisional PatentApplication No. 202241003166, filed Jan. 19, 2022, the entire contentbeing incorporated herein by reference.

BACKGROUND

A sensor such as radar can detect an object by transmitting a signal andreceiving a reflection of the transmitted signal. A nearby transmittercan impair the performance of the sensor if a signal generated by thetransmitter is in the same frequency band as the signal generated by thesensor. This interference can obscure the sensor's ability to detect anobject because the sensor cannot distinguish between transmitted signalsand reflected signals. Thus, the sensor becomes temporarily blinded bythe nearby transmitter.

One environment that provides an example of this interference isautomotive radar. Numerous automobiles equipped with radar sensors maybe traveling on the same roadway, potentially causing the transmitter ofeach radar sensor to interfere with the receivers of other vehicles. Thenumber of cars on the road equipped with radar is expected to increasein coming years, especially with safety standards requiring radarsensors for certain classifications.

SUMMARY

In some examples, a method includes performing, by processing circuitry,a first transform operation on a first time-domain data set to generatea frequency-domain data set. In addition, the method includesdetermining, by the processing circuitry, that at least one portion ofthe frequency-domain data set satisfies a first threshold magnitude. Themethod also includes performing, by the processing circuitry, an inversetransform operation on the at least one portion of the frequency-domaindata set to generate a second time-domain data set. The method furtherincludes identifying, by the processing circuitry and based on thesecond time-domain data set, a region of interference in the firsttime-domain data set.

In further examples, a device includes a receiver configured to generatean analog signal based on received signals. The device also includes ananalog-to-digital converter configured to convert the analog signal to afirst time-domain data set. The device further includes processingcircuitry configured to perform a first transform operation on the firsttime-domain data set to generate a frequency-domain data set. Inaddition, the processing circuitry is configured to determine that atleast one portion of the frequency-domain data set satisfies a firstthreshold magnitude. The processing circuitry is also configured toperform an inverse transform operation on the at least one portion ofthe frequency-domain data set to generate a second time-domain data set.The processing circuitry is further configured to identify, based on thesecond time-domain data set, a region of interference in the firsttime-domain data set.

In yet further examples, a non-transitory computer-readable medium hasexecutable instructions stored thereon, configured to be executable byprocessing circuitry for causing the processing circuitry to perform afirst transform operation on a first time-domain data set to generate afrequency-domain data set. In addition, the instructions cause theprocessing circuitry to determine that at least one portion of thefrequency-domain data set satisfies a first threshold magnitude. Theinstructions also cause the processing circuitry to perform an inversetransform operation on the at least one portion of the frequency-domaindata set to generate a second time-domain data set. The instructionsfurther cause the processing circuitry to identify, based on the secondtime-domain data set, a region of interference in the first time-domaindata set.

BRIEF DESCRIPTION OF THE DRAWINGS

Features of the present invention may be understood from the followingdetailed description and the accompanying drawings. In that regard:

FIG. 1 is a conceptual block diagram of interference caused byautomotive radar sensors according to some aspects of the presentdisclosure.

FIG. 2A is a graph of frequency of a continuous wave over time.

FIG. 2B is a graph of the magnitude of analog-to-digital converter (ADC)samples over time.

FIGS. 3 and 4 are conceptual diagrams of interference detectionprocesses according to some aspects of the present disclosure.

FIGS. 5A and 5B are conceptual block diagrams of two thresholdingoperations according to some aspects of the present disclosure.

FIG. 6 is a graph of the magnitude of a set of ADC samples showing aregion of interference according to some aspects of the presentdisclosure.

FIGS. 7-10 are conceptual diagrams of interference detection and repairprocesses according to some aspects of the present disclosure.

FIG. 11 is a conceptual block diagram of a detection system according tosome aspects of the present disclosure.

FIG. 12 is a flow diagram of a method of detecting interferenceaccording to some aspects of the present disclosure.

DETAILED DESCRIPTION

Specific examples are described below in detail with reference to theaccompanying figures. It is understood that these examples are notintended to be limiting, and unless otherwise noted, no feature isrequired for any particular example. Moreover, the formation of a firstfeature over or on a second feature in the description that follows mayinclude examples in which the first and second features are formed indirect contact and examples in which additional features are formedbetween the first and second features, such that the first and secondfeatures are not in direct contact.

Interference caused by a nearby transmitter can cause a sensor to betemporarily impaired. Often, this impairment lasts for a very shortperiod of time, particularly with frequency-modulated continuous wave(FMCW) transmitters. This disclosure describes techniques foridentifying data samples that are impaired by interference. Thesetechniques can include transforming a data set from the time domain intothe frequency domain and performing a threshold operation to identify aportion of the frequency-domain data. Once the identified portion of thefrequency-domain data is transformed back into the time domain, theimpaired subset of data samples may be much more easily isolated andrepaired, as compared to simply thresholding the original time-domaindata set without transforming the data set into the frequency domain. Ofcourse, these advantages are merely examples, and no advantage isrequired for any particular embodiment.

Examples of interference detection are described with reference to thefigures below. In that regard, FIG. 1 is a conceptual block diagram ofinterference caused by automotive radar sensors according to someaspects of the present disclosure. Although FIG. 1 depicts cars on aroadway, the techniques of this disclosure are applicable to othervehicle environments such as high-speed travel on a highway or denseurban environments including pedestrians, cyclists, and buildings. Thetechniques of this disclosure are also applicable to other vehicles suchas trucks, construction equipment, motorcycles, bicycles, aircraft,marine vehicles, drones, unmanned vehicles, autonomous vehicles, or evenspacecraft. The techniques of this disclosure may also be applied torobotic equipment in an industrial setting, such as a factory,warehouse, or loading dock. In addition, although this disclosuredescribes techniques that can be used with a radar sensor, thetechniques of this disclosure can be used with other sensors, such aslidar, ultrasound, cameras (e.g., visual light and/or infrared), and/orany other sensors.

In the example shown in FIG. 1 , vehicles 100 and 110 are travelling inthe same direction on a roadway. Vehicle 100 may be following behindvehicle 110. Vehicle 100 may include an assisted driving capability suchas adaptive cruise control that determines a distance from vehicle 100to vehicle 110 based on signals 102 and 104. Vehicle 120 is traveling inthe opposite direction of vehicles 100 and 110. A transmitter onboardvehicle 120 may interfere with a detection system onboard vehicle 100.

A transmitter onboard ownship vehicle 100 transmits signal 102, whichreflects off vehicle 110 as reflection signal 104. A receiver onboardownship vehicle 100 receives reflection signal 104, which may include aportion of the energy of signal 102 that bounced off of vehicle 110.Processing circuitry onboard ownship vehicle 100 may determine thedistance between vehicles 100 and 110 based on characteristics ofreflection signal 104. These characteristics may include the frequencyof reflection signal 104 and/or the frequency difference between signals102 and 104. The processing circuitry may also determine the azimuthangle of vehicle 110 relative to vehicle 100, the elevation angle ofvehicle 110 relative to vehicle 100, and/or the velocity of vehicle 100.

Vehicle 120 is also equipped with a transmitter that sends signal 122towards ownship vehicle 100. Additionally or alternatively, signal 122may include noise generated by vehicle 120, and the receiver onboardownship vehicle 100 receives the noise. In examples in which a frequencyof signal 122 is within a frequency band used by the sensor onboardownship vehicle 100, signal 122 may interfere with the detection systemonboard ownship vehicle 100. The magnitude of signal 122, as received byvehicle 100, may be stronger or weaker than the magnitude of reflectionsignal 104, as received by vehicle 100. Signal 122 may raise the noisefloor of the frequency spectrum of signals received by the detectionsystem onboard ownship vehicle 100, which reduces the signal-to-noiseratio and makes it less likely that processing circuitry onboard ownshipvehicle 100 can detect objects such as vehicle 110. On an actualroadway, there may be many more objects and transmitters than shown inFIG. 1 . Thus, it is desirable for the detection system onboard vehicle100 to coexist and function properly even in the face of interferingsignals from a nearby transmitter.

FIG. 2A is a graph 200 of frequency of a continuous wave over time. Ramp210 represents the frequency of a chirp transmitted by an FMCW radar. InFIG. 2A, ramp 210 is shown with a frequency that varies linearly withtime. In-band frequency range 212 represents the frequencies of interestto the detection system, such as a few tens of megahertz below thetransmitted frequency of ramp 210 because the receiver receives adelayed version of ramp 210. The down-conversion, low-pass filtering,and processing of the receiver may affect the range of in-band frequencyrange 212. In-band frequency range 212 varies linearly with time, justlike ramp 210, because the detection system is interested in objectswithin certain ranges. Ranges that are not of interest include objectsthat are very close to the sensor (e.g., the bumper of the ownshipvehicle) or very far from the sensor, such as several kilometers away,depending on the application.

A crossing interferer transmits a continuous wave that is represented byramp 220. The slope of ramp 220 is different from the slope of ramp 210,such that ramp 220 passes through in-band frequency range 212 for ashort duration bounded by times 270 and 280. Ramp 220 crosses ramp 210around time 280, and ramp 220 is within in-band frequency range 212 fora short duration before crossing ramp 210. Additional example details ofcontinuous wave radar can be found in commonly assigned U.S. Pat. No.11,125,856, entitled “Distance Measurement Using Millimeter Wave Radar,”issued on Sep. 21, 2021; U.S. Pat. No. 11,054,500, entitled “NoiseMeasurement in a Radar System,” issued Jul. 6, 2021; and U.S. Pat. No.10,768,278, entitled “Field Monitoring of Analog Signals in a RadarSystem,” issued on Sep. 8, 2020, each of which is incorporated byreference in its entirety.

Crossing interferers may be relatively common where a technical standardrequires that sensors use the same frequency band. For example, someautomotive radar use a frequency band that includes seventy-sevengigahertz, according to regulations made by Federal CommunicationsCommission or the European Telecommunications Standard Institute. Radarinterference events occur when the radar modules on multiple vehiclesare using the same frequency band in a close area simultaneously. Eachradar module may transmit a succession of chirps, where the duration,frequency range, and slope of each chirp may vary across radar modules.Additionally, each radar module may change these parameters acrosschirps. In other words, each radar chirp may have differentcharacteristics in order to avoid consistently interfering with thechirps of nearby sensors.

FIG. 2B is a graph 250 of the magnitude of analog-to-digital converter(ADC) samples 260 over time. The magnitude of ADC samples 260 representthe digital numbers outputted by an ADC, which in turn represent themagnitude of a sampled analog signal received by an ADC from a receiver.The magnitude of ADC samples 260 is larger between times 270 and 280, ascompared to other times in graph 250, because of the interference causedby ramp 220 passing through in-band frequency range 212. Thecorresponding ADC samples between times 270 and 280 are thereforecorrupted because the noise floor has been raised, potentially buryingactual reflectors in the noise. Graph 250 is one example of the effectof interference—in some instances, the effect of interference may beless noticeable in the time-domain data.

Even when the effect on the time-domain data is less noticeable, theinterference may still impair the detection of objects, especially weakreflectors. In other words, the interference experienced by the receiverbetween times 270 and 280 impairs the ability to detect objects,especially the detection of weak reflectors when strong reflectors arealso present. A weak reflector will show up in the frequency spectrum,except when interference raises the noise floor of the spectrum.Interference can occur for a small portion of a chirp (e.g., tenpercent) and still raise the noise floor across the frequency spectrumenough to bury or hide a weak reflector. Weak reflectors may be smallerobjects, less reflective objects (e.g., plants or animals), stationaryobjects, and/or objects that are far away from the ownship vehicle. Incontrast, strong reflectors may be larger objects, more reflectiveobjects (e.g., metal or concrete), moving objects, and close-rangeobjects.

FIG. 3 is a conceptual diagram of an interference detection processaccording to some aspects of the present disclosure. Region ofinterference 302 in FIG. 3 is limited in time and impacts only some oforiginal time-domain data set 300. Although the term “region” is used todescribe region of interference 302, region of interference 302represents a time span (e.g., a set of consecutive ADC samples), ratherthan a physical area. FIG. 3 depicts a single region of interference,but original time-domain data set 300 may include multiple regions ofinterference caused by, for example, multiple crossing interferers or asingle crossing interferer that interferes twice within a single chirp.

The values of original time-domain data set 300 in region ofinterference 302 may be higher than other regions, but standardthresholding may not be sufficient to accurately detect the extent ofregion of interference 302. Instead of merely thresholding time-domaindata set 300, the process depicted in FIG. 3 includes converting thedata to the frequency domain to remove a portion of spectrum. Theprocess depicted in FIG. 3 includes detecting and eliminating strongreflectors from original time-domain data set 300 to generate modifiedtime-domain data set 360, from which region of interference 302 can bemore easily detected.

Processing circuitry may be configured to receive original time-domaindata set 300 from an ADC, and the processing circuitry may storeoriginal time-domain data set 300 to a memory. The processing circuitrymay be configured to perform transform operation 310 (e.g., a FastFourier Transform (FFT)) on original time-domain data set 300 togenerate frequency-domain data set 320. Although FIGS. 3, 4, and 7-10show the transform operations as FFT operations, other transformoperations such as a Laplace transform or a Z-transform may be used toconvert time-domain data to frequency-domain data.

The processing circuitry can perform threshold operation 330 todetermine that the magnitude of the frequency bins shown as peaks 322,324, and 326 are greater than threshold magnitude 328. The processingcircuitry may be configured to select the threshold magnitude in one ofmany ways. For example, the threshold could be a constant across theentire frequency-domain data, with the value of the constant being acertain dB (for example 6 dB) below the highest peak in frequency-domaindata set 320. Alternatively the threshold could be an adaptivethreshold—selected per frequency bin based on a constant false alarmrate (CFAR) detector. The CFAR detector looks at the average signalvalue in the vicinity of the frequency bin to determine the threshold.The processing circuitry can determine that peaks 322, 324, and 326 donot satisfy threshold magnitude 328 because the magnitudes associatedwith peaks 322, 324, and 326 are not less than threshold magnitude 328.Responsive to determining that peaks 322, 324, and 326 do not satisfythreshold magnitude 328, the processing circuitry can perform thresholdoperation 330 by suppressing or zeroing-out the frequency binsassociated with peaks 322, 324, and 326 from frequency-domain data set320. Zeroing out the frequency bins can lead to abrupt step changes inthe signal level in the frequency samples. These abrupt changes can leadto sidelobes in the inverse transform output. In order to reduce suchsidelobes, the processing circuitry can optionally use a smootheningwindow to reduce such abrupt changes.

The strong reflectors associated with peaks 322, 324, and 326 arelimited to a few frequency bins in the spectrum while the interferenceis spread across all frequency bins. In some examples, peak 322 isassociated with the bumper of the ownship vehicle. The signalscorresponding to the peaks 322, 324 and 326 are present across theentire chirp (i.e., across all ADC samples of the chirp). In contrast,the interference signal may be much weaker than peak 322, and theinterference signal may be transitory (e.g., only a few ADC samples areaffected).

The processing circuitry performs threshold operation 330 on thefrequency-domain data set 320 to generate low-threshold frequency-domaindata set 340, which includes the spectrum of frequency-domain data set320, except for the magnitudes of peaks 322, 324, and 326. The magnitudeof peaks 322, 324, and 326 has been zeroed out in low-thresholdfrequency-domain data set 340. Thus, low-threshold frequency-domain dataset 340 includes one or more portions of frequency-domain data set 320,while three portions (i.e., peaks 322, 324, and 326) have been removed.

The processing circuitry may be configured to perform inverse transform350 on low-threshold frequency-domain data set 340 to generate modifiedtime-domain data set 360. In some examples, time-domain data set 360 hasthe same number of samples as time-domain data set 300. Although FIGS.3, 4, and 7-10 show the inverse transform operations as inverse FFT(IFFT) operations, other transform operations such as an inverse Laplacetransform, or an inverse Z-transform may be used to convert frequencydomain-data to time-domain data. The processing circuitry can performthresholding operation 370 to identify region of interference 362. Forexample, the processing circuitry can perform thresholding operation 370by applying an absolute-value filter or an absolute-value differencefilter or any absolute-value high pass filter to modified time-domaindata set 360. Thresholding operation 370 may accentuate region ofinterference 362, making it easier to identify the time period(s) wheninterference occurs. Thus, the process shown in FIG. 3 may be moreaccurate and/or reliable in detecting a region of interference, ascompared to conventional thresholding.

After region of interference 302 or 362 is identified, the processingcircuitry may be configured to repair original time-domain data set 300by, for example, inserting a reconstructed region into originaltime-domain data set 300. The processing circuitry can replace a seriesof consecutive ADC samples of original time-domain data set 300 with thereconstructed region, where the series of samples and the reconstructionregion are both associated with the same time duration. After repairingoriginal time-domain data set 300, the processing circuitry can detectobjects based on the repaired data.

The detection processes shown in FIGS. 3, 4, and 7-10 can be used on aper-chirp basis, meaning that the detection process does not need thedata from multiple chirps before implementing the detection process.Processing circuitry can perform a detection process shown in any ofFIGS. 3, 4, and 7-10 after receiving each chirp. The processingcircuitry may be able to perform the detection process without anyhistory information beyond the boundaries of the chirp. Alternatively,the processing circuitry can compile the data from multiple chirps andperform a detection process shown in any of FIGS. 3, 4, and 7-10 on thecompiled data. Moreover, the detection processes shown in FIGS. 3, 4,and 7-10 may be reliable even when there are multiple targets atdifferent velocities in the same range bin.

FIG. 4 is a conceptual diagram of an interference detection processaccording to some aspects of the present disclosure. The processdepicted in FIG. 4 includes detecting and eliminating weak reflectorsfrom original time-domain data set 400 to generate modified time-domaindata set 480, from which region of interference 402 can be more easilydetected. Time-domain data set 480 is generated by performingsubtraction operation 470 on time-domain data sets 400 and 460.

Processing circuitry may be configured to perform transform operation410 on original time-domain data set 400 to generate frequency-domaindata set 420. The processing circuitry can perform threshold operation430 to determine that peaks 422, 424, and 426 are greater than thresholdmagnitude 428. In some examples, the processing circuitry can determinethat peaks 422, 424, and 426 satisfy threshold magnitude 428 because themagnitudes associated with peaks 422, 424, and 426 are greater thanthreshold magnitude 428. Responsive to determining that peaks 422, 424,and 426 satisfy threshold magnitude 428, the processing circuitry canperform threshold operation 430 by zeroing-out all of the otherfrequency bins that are not associated with peaks 422, 424, and 426. Insome examples, threshold operation 430 is an inverted form of thresholdoperation 330 shown in FIG. 3 . In order to reduce such sidelobes, theprocessing circuitry can optionally use a smoothening window during thezeroing-out operation.

The processing circuitry performs threshold operation 430 on thefrequency-domain data set 420 to generate high-thresholdfrequency-domain data set 440, which includes only peaks 422, 424, and426. The magnitude of all other frequency components has been zeroed outin high-threshold frequency-domain data set 440. The processingcircuitry may be configured to perform inverse transform 450 onhigh-threshold frequency-domain data set 440 to generate modifiedtime-domain data set 460. The processing circuitry performs subtractionoperation 470 to generate time-domain data set 480 by subtractingtime-domain data set 460 from original time-domain data set 400. Then,the processing circuitry can perform thresholding operation 490 toidentify region of interference 482 in time-domain data set 480.

After region of interference 402 or 482 is identified, the processingcircuitry may be configured to repair original time-domain data set 400by, for example, inserting a reconstructed region into originaltime-domain data set 400. Although the detection process shown in FIG. 4includes more steps than the process shown in FIG. 3 , the process shownin FIG. 4 may be advantageous for detecting the objects associated withpeaks 422, 424, and 426, and generating a reconstructed region based ontime-domain data set 460. After repairing original time-domain data set400, the processing circuitry can detect objects based on the repaireddata.

FIGS. 5A and 5B are conceptual block diagrams of two thresholdingoperations according to some aspects of the present disclosure. Althoughthe thresholding operations of FIGS. 5A and 5B are described as beingperformed on time-domain data, these thresholding operations may beperformed on frequency-domain data. For example, thresholding operations330, 430, 725, 825, 925, and/or 1025 are performed on frequency-domaindata. Thresholding operations 330, 430, 725, 825, 925, and/or 1025 caninclude one or more of the steps of the thresholding operations shown inFIGS. 5A and 5B.

In FIG. 5A, processing circuitry performs absolute-value operation 510and threshold operation 520 on time-domain data 500. Each value oftime-domain data 500 may be a complex value or just a signed value,where the absolute value of a complex value is equal to the square rootof the sum of squares of the real and complex units. The output ofabsolute-value operation 510 may be a filtered time-domain data setconsisting of the absolute value of the input. Processing circuitry maybe configured to compare the output of absolute-value operation 510 to athreshold magnitude to identify the region of interference. In someexamples, the magnitude of the ADC samples in the region of interferenceare greater than the ADC sample values in other regions because acrossing interferer (e.g., a nearby transmitter) elevates the noisefloor, allowing for detection using absolute-value operation 510 andthreshold operation 520.

In FIG. 5B, processing circuitry performs differencing operation 560,absolute-value operation 570, and threshold operation 580 on time-domaindata 550. Processing circuitry may be configured to perform differencingoperation 560 by subtracting each ADC sample value from a previous orsubsequent ADC sample value to generate a differential time-domain dataset. Alternatively, differencing operation 560 may be implemented asx(n)−x(n−A), where A is any integer other than zero.

Performing the thresholding operations shown in FIGS. 5A or 5B alone mayallow for the identification of regions of strong interference in theabsence of any strong reflectors. However, in the presence of a largenumber of objects (e.g., an urban environment) or strong reflectors, orwhen the interference is relatively weak, the thresholding operationsshown in FIGS. 5A or 5B alone may not be sufficient for identifyingregions of interference, or the interference may be barely detectable. Aregion of relatively weak interference may not stand out in time-domaindata 500 or 550. To more effectively identify a region of interference,processing circuitry may perform the transform operations and thresholdoperations shown in FIG. 3 or 4 before performing the thresholdingoperations shown in FIGS. 5A or 5B. Thus, the processing circuitry maybe configured to perform the thresholding operations shown in FIGS. 5Aor 5B on modified time-domain data set 360 or 480, rather than onoriginal time-domain data set 300 or 400.

FIG. 6 is a graph 600 of the magnitudes (logarithmic representation) ofa set of ADC samples showing a region of interference according to someaspects of the present disclosure. The ADC samples shown in graph 600may be the output of a thresholding operation, such as the thresholdingoperations shown in FIG. 5B. As shown in FIG. 6 , graph 600 includesthree regions: region 610 encompassing approximately the first ninehundred ADC samples, region 620 encompassing approximately the next twohundred ADC samples, and region 630 encompassing approximately the finalone thousand ADC samples. The ADC samples in regions 610 and 630 havemagnitudes of less than negative fifteen dB. In contrast, some of theADC samples in region 620 have magnitudes greater than negative five dB.This difference in ADC sample magnitudes creates a clearly detectableregion of interference corresponding to region 620.

The frequency components associated with strong reflectors have beenremoved from the frequency spectrum of the ADC samples shown in graph600. For example, processing circuitry may have performed a transformoperation on the original ADC samples to generate a frequency-domaindata set. The processing circuitry may have then identified and removedstrong reflectors from the frequency-domain data set by performing athreshold operation to generate a modified frequency-domain data set.The processing circuitry could have generated the ADC samples shown ingraph 600 by performing an inverse transform operation on the modifiedfrequency-domain data set.

FIGS. 7-10 are conceptual diagrams of interference detection and repairprocesses according to some aspects of the present disclosure. Theprocesses depicted in FIGS. 7-10 include detecting and eliminatingstrong reflectors from original time-domain data sets 700, 800, 900, and1000 to make it easier to detect one or more regions of interference.Even though the magnitude of ADC samples in a region of interference maybe higher than other regions, conventional thresholding alone may not besufficient to accurately detect the region of interference. FIGS. 7-10depicts four example processes for identifying and repairing a region ofinterference.

In the example shown in FIG. 7 , processing circuitry receives originaltime-domain data set 710 from one or more ADCs. The processing circuitryperforms transform operation 715 on original time-domain data set 710 togenerate frequency-domain data set 720. The processing circuitry thenperforms threshold operation 725 to generate frequency-domain data sets730 and 732. The processing circuitry can identify frequency-domain dataset 730 based on threshold operation 725 in frequency-domain data set720. The processing circuitry can identify a remaining portion offrequency-domain data set 720 as frequency-domain data set 732.Frequency-domain data set 730 includes data for the frequency bins withmagnitudes less than, or less than or equal to, the threshold magnitude.Frequency-domain data set 730 may include the frequency components ofweak reflectors and interference. Frequency-domain data set 732 includesdata for the frequency bins with magnitudes greater than, or greaterthan or equal to, the threshold magnitude. Frequency-domain data set 732may include the frequency components of strong reflectors.

The processing circuitry can perform inverse transform operation 735 onfrequency-domain data set 730 to generate time-domain data set 740. Theprocessing circuitry can perform inverse transform operation 765 onfrequency-domain data set 732 to generate time-domain data set 770. Eachof time-domain data sets 740 and 770 may be a portion of originaltime-domain data set 710, such that original time-domain data set 710 isthe sum of the corresponding values of time-domain data sets 740 and770.

The processing circuitry may be configured to then perform thresholdingoperation 745 on time-domain data set 740 to identify region ofinterference 750. The processing circuitry can generate interferenceindicator bits to identify the ADC samples that may have been affectedby interference. To identify region of interference 750, the processingcircuitry determines one or more portions of time-domain data set 740that satisfies a threshold. Additionally or alternatively, theprocessing circuitry can perform the thresholding operation shown inFIG. 5A or the thresholding operation shown in FIG. 5B to identifyregion of interference 750. The processing circuitry can perform anaveraging operation, a low-pass filtering operation, and/or a smoothingoperation before performing the threshold operation shown in FIG. 5A or5B.

After identifying region of interference 750, the processing circuitrycan generate undamaged portion 760, which is the portion of originaltime-domain data set 710 that does not include the data pointsassociated with region of interference 750. The processing circuitry maybe configured to generate undamaged portion 760 by setting the ADCsamples associated with region of interference 750 to zero in originaltime-domain data set 710. The processing circuitry can use region ofinterference 750 to generate reconstructed region 780 by identifying thedata points in time-domain data set 770 that correspond to the datapoints associated with region of interference 750. As just one example,if each of time-domain data sets 710, 740, and 770 include two thousanddata points or ADC samples, and region of interference 750 covers twohundred consecutive data points from the nine hundredth to elevenhundredth points (see FIG. 6 ), the processing circuitry can generatereconstructed region 780 by storing the two hundred consecutive datapoints in time-domain data set 770 from the nine hundredth to elevenhundredth points.

The processing circuitry can generate repaired time-domain data set 790by inserting reconstructed region 780 into undamaged portion 760. Insome examples, the processing circuitry generates repaired time-domaindata set 790 by replacing region of interference 750 of originaltime-domain data set 710 with reconstructed region 780. The processingcircuitry may simply swap out region of interference 750 in originaltime-domain data set 710 for reconstructed region 780 to generaterepaired time-domain data set 790 without separately generatingundamaged portion 760.

In some examples, repaired time-domain data set 790 is identical tooriginal time-domain data set 710, except for the data pointscorresponding to region of interference 750. For each data pointcorresponding to region of interference 750, the time-domain value maybe equal to the corresponding value in original time-domain data set 710minus the corresponding value in time-domain data set 740. The value oftime-domain data set 740 for each data point in region of interference750 is an approximation of the signal strength attributable to theinterferer.

FIG. 8 is a conceptual diagram of an interference detection and repairprocess including a single inverse transform operation 835, whereas theprocess shown in FIG. 7 included two inverse transform operations 735and 765. In the example shown in FIG. 8 , processing circuitry performstransform operation 815 on original time-domain data set 810 to generatefrequency-domain data set 820. The processing circuitry then performsthreshold operation 825 on frequency-domain data set 820 to generatefrequency-domain data set 830, which may include the frequencycomponents of weak reflectors and interference. In the example shown inFIG. 8 , unlike the example shown in FIG. 7 , the processing circuitrydoes not generate a frequency-domain data set including the frequencycomponents of strong reflectors (e.g., frequency-domain data set 732).The processing circuitry can perform inverse transform operation 835 onfrequency-domain data set 830 to generate time-domain data set 840.

The processing circuitry performs subtraction operation 865 bysubtracting time-domain data set 840 from original time-domain data set810 to generate time-domain data set 870. Each of time-domain data sets840 and 870 may be a portion of original time-domain data set 810, suchthat original time-domain data set 810 is the sum of the correspondingvalues of time-domain data sets 840 and 870. The processing circuitrymay be configured to then perform thresholding operation 845 ontime-domain data set 840 to identify region of interference 850.

After identifying region of interference 850, the processing circuitrycan generate undamaged portion 860 by zeroing out the values of the datapoints in original time-domain data set 810 that are associated withregion of interference 850. The processing circuitry can generatereconstructed region 880 by identifying the data points in time-domaindata set 870 that correspond to the data points associated with regionof interference 850. The processing circuitry can generate repairedtime-domain data set 890 by inserting reconstructed region 880 intoundamaged portion 860 or into original time-domain data set 810. In someexamples, the processing circuitry generates repaired time-domain dataset 890 by replacing, with reconstructed region 880, the data points oforiginal time-domain data set 810 that are associated with region ofinterference 850.

The interference detection and repair process shown in FIG. 8 may beless computationally intensive than the interference detection andrepair process shown in FIG. 7 . For example, the process shown in FIG.8 includes only one inverse transform operation 835, whereas the processshown in FIG. 7 includes two inverse transform operations 735 and 765.The process shown in FIG. 8 includes subtraction operation 865 insteadof inverse transform operation 765, and subtraction operation 865 haslower computational overhead than inverse transform operation 765. Inaddition, the process shown in FIG. 8 may also have lower latency thanthe process shown in FIG. 7 because fewer operations are performed inFIG. 8 .

FIG. 9 is a conceptual diagram of an interference detection and repairprocess including a single inverse transform operation 935. Inversetransform operation 935 is performed on high-threshold frequency-domaindata, whereas inverse transform operation 835 in FIG. 8 is performed onlow-threshold frequency-domain data. In the example shown in FIG. 9 ,processing circuitry performs transform operation 915 on originaltime-domain data set 910 to generate frequency-domain data set 920. Theprocessing circuitry then performs threshold operation 925 onfrequency-domain data set 920 to generate frequency-domain data set 930,which may include the frequency components of strong reflectors. Theprocessing circuitry can perform inverse transform operation 935 onfrequency-domain data set 930 to generate time-domain data set 940.

The processing circuitry performs subtraction operation 945 bysubtracting time-domain data set 940 from original time-domain data set910 to generate time-domain data set 950. The processing circuitry maybe configured to then perform thresholding operation 955 on time-domaindata set 950 to identify region of interference 960. After identifyingregion of interference 960, the processing circuitry can generateundamaged portion 970 by zeroing out the values of the data points inoriginal time-domain data set 910 that are associated with region ofinterference 960. The processing circuitry can generate repairedtime-domain data set 990 by inserting reconstructed region 980 intoundamaged portion 970 or into original time-domain data set 910.

FIG. 10 is a conceptual diagram of an interference detection and repairprocess including a single inverse transform operation 1035. Inaddition, the process shown in FIG. 10 generates reconstructed region1070 without generating an entire reconstructed dominant signal (e.g.,time-domain data set 770, 870, or 940) In the example shown in FIG. 10 ,processing circuitry performs transform operation 1015 on originaltime-domain data set 1010 to generate frequency-domain data set 1020.The processing circuitry then performs threshold operation 1025 onfrequency-domain data set 1020 to generate frequency-domain data set1030, which may include the frequency components of weak reflectors andinterference. The processing circuitry also performs inverse transformoperation 1035 on frequency-domain data set 1030 to generate time-domaindata set 1040.

The processing circuitry may be configured to then perform thresholdingoperation 1045 on time-domain data set 1040 to identify region ofinterference 1050. After identifying region of interference 1050, theprocessing circuitry can generate undamaged portion 1060 by zeroing outthe values of the data points in original time-domain data set 1010 thatare associated with region of interference 1050. The processingcircuitry can perform subtraction operation 1065 to generatereconstructed region 1070 by subtracting the values of time-domain dataset 1040 within region of interference 1050 from the values of thecorresponding data points in original time-domain data set 1010. Theprocessing circuitry can generate repaired time-domain data set 1080 byinserting reconstructed region 1070 into undamaged portion 1060 or intooriginal time-domain data set 1010.

The interference detection and repair process shown in FIG. 10 may beless computationally intensive with lower computational overhead thanthe interference detection and repair processes shown in FIGS. 7-9 . Forexample, the process shown in FIG. 10 includes only one inversetransform operation 1035 and does not generate an entire time-domaindata set for the strong reflectors. In contrast, the process shown inFIG. 8 generates time-domain data set 840 for weak reflectors andinterference and generates time-domain data set 870 for strongreflectors. Likewise, the process shown in FIG. 9 generates time-domaindata set 940 for strong reflectors and generates time-domain data set950 for weak reflectors and interference. The process shown in FIG. 10may also have lower latency than the processes shown in FIGS. 7-9because fewer operations are performed in the process shown in FIG. 10 .

FIG. 11 is a conceptual block diagram of a detection system 1100according to some aspects of the present disclosure. In the examplesystem of FIG. 11 , detection system 1100 includes receiver 1110, ADC1120, processing circuitry 1130, and memory 1140. Detection system 1100may also include other components not shown in FIG. 11 , such asadditional receivers, one or more transmitters, and/or any otheradditional components. Detection system 1100 may be configured to beinstalled in a larger system, such as an automobile, aircraft, marinevehicle, industrial facility, or robot (e.g., robotic arm).

Receiver 1110 is configured to receive signals from object 1150 andinterferer 1160 via one or more antennas. The one or more antennas maybe part of receiver 1110 or external to detection system 1100. Receiver1110 may include components such as an amplifier, an analog filter, amixer (e.g., for down-conversion), and/or a local oscillator circuit.The signals received by receiver 1110 may include or be a combination ofthe signals reflected off and/or transmitted by object 1150 andinterferer 1160. In this disclosure, receiver 1110 may also be referredto as a sensor because receiver 1110 senses signals transmitted by orreflected off object 1150 and interferer 1160.

ADC 1120 is configured to sample an analog signal received by ADC 1120from receiver 1110. ADC 1120 may receive and convert the analog signalsfrom receiver 1110 into a digitized stream of data. The digitized streamof data may include information on the location and velocity ofdifferent objects within the field of view of receiver 1110, such asobject 1150 and interferer 1160.

Processing circuitry 1130 may be configured to process the digitalsamples outputted by ADC 1120 to detect object 1150 and/or interferer1160. For example, processing circuitry 1130 may be configured toidentify and isolate each object or target by identifying range,velocity and angle of each object based on the digital numbers receivedfrom ADC 1120. Processing circuitry 1130 may be configured to performthe detection process(es) shown in FIGS. 3, 4, 7, 8, 9 , and/or 10. Forexample, processing circuitry 1130 may be configured to performtransform operation(s), threshold operation(s), inverse transformoperation(s), thresholding operation(s) filtering operation(s),subtraction operation(s), and/or insertion operation(s) to detectinterference and repair a data set affected by interference.

Memory 1140 may be coupled to processing circuitry 1130 via a bus.Memory 1140 may include instruction memory for storing instructions thatare executable by processing circuitry 1130. For example, theinstruction stored by memory 1140 include performing one or morefunctions such as the performance of transform, threshold, inversetransform, thresholding, filtering, subtraction, and/or insertionoperations. Memory 1140 may also include data memory for storingtime-domain data sets and frequency-domain data sets.

Object 1150 may be a moving object or a stationary object, such as avehicle, a person, an animal, a building, debris, or terrain. Interferer1160 may also be a moving object or a stationary object with atransmitter or other noise generating device. In some examples,interferer 1160 generates electromagnetic energy in the frequency bandof interest to receiver 1110. In the example of automotive radar,receiver 1110 may operate in a frequency band from seventy-six orseventy-seven gigahertz to eighty-one gigahertz. Although thisdisclosure describes techniques for detecting interference, detectionsystem 1100 may be configured to also detect periods of noise (e.g.,white noise) experienced by receiver 1110.

FIG. 12 is a flow diagram of a method of detecting interferenceaccording to some aspects of the present disclosure. Some processes ofthe method 1200 may be performed in orders other than described, andmany processes may be performed concurrently in parallel. Furthermore,processes of the method 1200 may be omitted or substituted in someexamples of the present disclosure. The method 1200 is described withreference to the operations shown in FIG. 10 being performed bydetection system 1100 shown in FIG. 11 , although other components suchas vehicle 100 shown in FIG. 1 may exemplify similar techniques.

Referring to block 1210, processing circuitry 1130 performs transformoperation 1015 on original time-domain data set 1010 to generatefrequency-domain data set 1020. After receiving original time-domaindata set 1010 from ADC 1120, processing circuitry 1130 may be configuredto store original time-domain data set 1010 to the data memory of memory1140. Processing circuitry 1130 can perform transform operation 1015 byimplementing instructions stored in memory 1140. Each data point offrequency-domain data set 1020 may associate a frequency bin (e.g., arange of frequencies) with a magnitude representing the signal strengthreceived by receiver 1110 at that frequency or range of frequencies.Processing circuitry 1130 may be configured to also storefrequency-domain data set 1020 to the data memory of memory 1140 afterperforming transform operation 1015.

Referring to block 1220, processing circuitry 1130 determines that aportion of frequency-domain data set 1020 satisfies a thresholdmagnitude. Processing circuitry 1130 can perform threshold operation1025 by comparing the magnitude of each data point of frequency-domaindata set 1020 to a threshold magnitude. Processing circuitry 1130generates frequency-domain data set 1030 by isolating the data points offrequency-domain data set 1020 that satisfy or do not satisfy thethreshold magnitude. For example, processing circuitry 1130 candetermine that each data point of frequency-domain data set 1030 is lessthan the threshold magnitude. By isolating the data points that are lessthan the threshold magnitude, processing circuitry 1130 can generatefrequency-domain data set 1030 using frequency-domain data set 1020.Frequency-domain data set 1030 is a portion of frequency-domain data set1020 because frequency-domain data set 1030 includes fewer than all ofthe data points of frequency-domain data set 1020.

Referring to block 1230, processing circuitry 1130 performs inversetransform operation 1035 on frequency-domain data set 1030 to generatetime-domain data set 1040. Processing circuitry 1130 can perform inversetransform operation 1035 by implementing an IFFT process onfrequency-domain data set 1030. Time-domain data set 1040 may have thesame number of data points as original time-domain data set 1010 becausetime-domain data set 1040 may cover the same time span (e.g., the timeduration of a radar chirp). The values of some or all of the data pointsmay be different in time-domain data sets 1010 and 1040. The amplitudeof time-domain data set 1040 may approximately represent the amplitudeor strength of signals received by receiver 1110 from weak reflectorsand interferers (e.g., interferer 1160). The amplitude or strength ofsignals received by receiver 1110 from strong reflectors (e.g., object1150) may be absent from time-domain data set 1040.

Referring to block 1240, processing circuitry 1130 identifies region ofinterference 1050 in time-domain data set 1010 based on time-domain dataset 1040. Region of interference 1050 is a span of data points intime-domain data set 1010 and/or 1040 that were possibly affected byinterference or noise. Region of interference 1050 may be a portion oftime-domain data set 1040 with relatively large magnitude values. Theselarge magnitude values indicate that the signals received by receiver1110 from interferer 1160 during the time duration associated withregion of interference 1050 were within a frequency band of interest todetection system 1100, as discussed with respect to continuous-waveradar in FIG. 2 .

In some examples, processing circuitry 1130 performs thresholdingoperation 1045 by determining the absolute value associated with eachdata point. In addition, to implement filtering operation 1045,processing circuitry 1130 may be configured to perform a differencingoperation on time-domain data set 1040 (e.g., similar to taking atime-derivative) and/or perform a smoothing operation. Processingcircuitry 1130 can perform thresholding operation 1045 by comparing theresulting data points to a threshold magnitude. Processing circuitry1130 can identify region of interference 1050 by identifying the datapoints in time-domain data set 1040 with relatively large absolutevalues or by identifying data points in the first difference of thetime-domain data with relatively large absolute values. Processingcircuitry 1130 may be configured to identify these data points bycomparing these points to threshold. This threshold can be constantacross the entire data set, or it can be variable, being set by a CFARdetector. If the threshold is constant, processing circuitry 1130 canderive the threshold from the mean values of the data points or from themean absolute values of the first difference of the data points.

Referring to optional repair block 1250, processing circuitry 1130subtracts at least a portion of time-domain data set 1040 fromtime-domain data set 1010 to generate reconstructed region 1070.Processing circuitry 1130 can perform subtraction operation 1065 bysubtracting the value of each data point in time-domain data set 1040that is associated with region of interference 1050 from eachcorresponding data point in time-domain data set 1010 (e.g., each datapoint in time-domain data set 1010 that is associated with region ofinterference 1050). Reconstructed region 1070 may include only datapoints within region of interference 1050, while other data pointsoutside of region of interference 1050 may be excluded fromreconstructed region 1070 or have values set to zero or some othervalue.

Referring to optional repair block 1260, processing circuitry 1130inserts reconstructed region 1070 into original time-domain data set1010. In some examples, processing circuitry 1130 generates repairedtime-domain data set 1080 by replacing a portion of time-domain data set1010 with reconstructed region 1070. Processing circuitry 1130 cangenerate repaired time-domain data set 1080 by setting each time-domainvalue in region of interference 1050 of original time-domain data set1010 to the time-domain value of the corresponding data point inreconstructed region 1070. Processing circuitry 1130 may be configuredto refrain from modifying the data points of original time-domain dataset 1010 outside of region of interference 1050 because those datapoints may not have been affected by the signals transmitted byinterferer 1160.

The signals received by receiver 1110 within region of interference 1050may be the only signals that processing circuitry 1130 has identified asaffected by interferer 1160. As discussed with respect to FIG. 2 ,interferer 1160 may have transmitted signals during the entire timeduration represented by time-domain data set 1010, but the frequency ofthe transmitted signals may overlap with the frequency band of interestto detection system 1100 only during the time duration represented byregion of interference 1050. Thus, the data points outside region ofinterference 1050 may not need any repair.

Processing circuitry 1130 may be configured to detect object 1150 andother objects based on time-domain data set 1080 using, for example, anFFT constant false alarm rate algorithm. Processing circuitry cangenerate a graphical user interface (GUI) based on the detected objectsand output the GUI to a display. The display may be part of detectionsystem 1100 or external to detection system 1100. Additionally oralternatively, processing circuitry 1130 may be configured to generatean alert based on the location and/or velocity of object 1150.Processing circuitry 1130 may be configured to also transmit data aboutthe location and velocity of the detected objects to an external system,such as to another vehicle or to a traffic management system.

The following numbered aspects demonstrate one or more aspects of thedisclosure.

Aspect 1. A method includes performing a first transform operation on afirst time-domain data set to generate a frequency-domain data set. Inaddition, the method includes determining that at least one portion ofthe frequency-domain data set satisfies a first threshold magnitude. Themethod also includes performing an inverse transform operation on the atleast one portion of the frequency-domain data set to generate a secondtime-domain data set. The method further includes identifying, based onthe second time-domain data set, a region of interference in the firsttime-domain data set.

Aspect 2. The method of the preceding aspect, wherein determining thatthe at least one portion of the frequency-domain data set satisfies thefirst threshold magnitude comprises performing a thresholding operationon the frequency-domain data set to identify the at least one portion ofthe frequency-domain data set.

Aspect 3. The method of either of the preceding aspects, whereinidentifying the region of interference comprises performing athresholding operation on the second time-domain data set to identifythe region of interference.

Aspect 4. The method of the preceding aspects or any combinationthereof, further including performing a differencing operation on thesecond time-domain data set to generate a differential time-domain dataset.

Aspect 5. The method of the preceding aspects or any combinationthereof, further including performing an absolute-value operation on thesecond time-domain data set or on the differential time-domain data setto generate a filtered time-domain data set.

Aspect 6. The method of the preceding aspect, further includingidentifying the region of interference by at least determining that aportion of the filtered time-domain data set satisfies a secondthreshold magnitude.

Aspect 7. The method of the preceding aspect, wherein determining thatthe portion of the filtered time-domain data set satisfies the secondthreshold magnitude comprises performing a second thresholding operationon the filtered time-domain data set to identify the portion of thefiltered time-domain data set.

Aspect 8. The method of the preceding aspects or any combinationthereof, further including subtracting a portion of the secondtime-domain data set from the region of interference in the firsttime-domain data set to generate a reconstructed region.

Aspect 9. The method of the preceding aspect, further includinginserting the reconstructed region into the first time-domain data set.

Aspect 10. The method of the preceding aspects or any combinationthereof, further including subtracting the second time-domain data setfrom the first time-domain data set to generate a third time-domain dataset.

Aspect 11. The method of the preceding aspect, further includingperforming a thresholding operation on the third time-domain data set togenerate a filtered time-domain data set.

Aspect 12. The method of the preceding aspect, wherein identifying theregion of interference is based on the filtered time-domain data set.

Aspect 13. The method of the aspect 10, further including identifying aportion of the third time-domain data set corresponding to the region ofinterference in the first time-domain data set.

Aspect 14. The method of the preceding aspect, further includinginserting the portion of the third time-domain data set into the firsttime-domain data set.

Aspect 15. The method of the preceding aspects or any combinationthereof, determining that a remaining portion of the frequency-domaindata set does not satisfy the first threshold magnitude.

Aspect 16. The method of the preceding aspect, wherein the remainingportion of the frequency-domain data set includes frequency bins not inthe at least one portion of the frequency-domain data set.

Aspect 17. The method of either of the two preceding aspects, furtherincluding performing the inverse transform operation on the remainingportion of the frequency-domain data set to generate a third time-domaindata set.

Aspect 18. The method of the preceding aspect, further includingidentifying a portion of the third time-domain data set corresponding tothe region of interference in the first time-domain data set.

Aspect 19. The method of the preceding aspect, further includinginserting the portion of the third time-domain data set into the firsttime-domain data set.

Aspect 20. A method includes performing a first transform operation on afirst time-domain data set to generate a first frequency-domain dataset. In addition, the method includes determining that at least onefrequency bin in the first frequency-domain data set satisfies a firstthreshold magnitude and generating a second frequency-domain data setincluding the at least one frequency bin and suppressed values for otherfrequency bins. The method also includes performing an inverse transformoperation on the second frequency-domain data set to generate a secondtime-domain data set. The method further includes identifying a regionof interference in the second time-domain data set.

Aspect 21. The method of the preceding aspect, wherein determining thatthe at least one frequency bin in the first frequency-domain data setsatisfies the first threshold magnitude comprises selecting one or morefrequency bins in the first frequency-domain data set having magnitudesthat are less than the first threshold magnitude.

Aspect 22. The method of aspect 20, wherein determining that the atleast one frequency bin in the first frequency-domain data set satisfiesthe first threshold magnitude comprises selecting one or more frequencybins in the first frequency-domain data set having magnitudes that areless than or equal to the first threshold magnitude.

Aspect 23. The method of any of the three preceding aspects, furtherincluding subtracting the region of interference in the secondtime-domain data set from a corresponding region in the firsttime-domain data set to generate a reconstructed region.

Aspect 24. The method of the preceding aspect, further includinginserting the reconstructed region into the first time-domain data setto generate a repaired time-domain data set.

Aspect 25. A device includes a receiver configured to generate an analogsignal based on received signals. The device also includes ananalog-to-digital converter configured to convert the analog signal to afirst time-domain data set. The device further includes processingcircuitry configured to perform the method of the preceding aspects orany combination thereof.

Aspect 26. A device includes a receiver configured to generate an analogsignal based on received signals. The device also includes ananalog-to-digital converter configured to convert the analog signal to afirst time-domain data set. The device further includes processingcircuitry configured to perform a first transform operation on the firsttime-domain data set to generate a frequency-domain data set. Inaddition, the processing circuitry is configured to determine that atleast one portion of the frequency-domain data set satisfies a firstthreshold magnitude. The processing circuitry is also configured toperform an inverse transform operation on the at least one portion ofthe frequency-domain data set to generate a second time-domain data set.The processing circuitry is further configured to identify, based on thesecond time-domain data set, a region of interference in the firsttime-domain data set.

Aspect 27. The device of either of the two preceding aspects, whereinthe receiver comprises a radar receiver.

Aspect 28. The device of any of the three preceding aspects, wherein thedevice is part of an automotive radar configured to be installed on anautomobile.

Aspect 29. A non-transitory computer-readable medium has executableinstructions stored thereon, configured to be executable by processingcircuitry for causing the processing circuitry to perform the method ofaspects 1-24 or any combination thereof.

Aspect 30. A non-transitory computer-readable medium has executableinstructions stored thereon, configured to be executable by processingcircuitry for causing the processing circuitry to perform a firsttransform operation on a first time-domain data set to generate afrequency-domain data set. In addition, the instructions cause theprocessing circuitry to determine that at least one portion of thefrequency-domain data set satisfies a first threshold magnitude. Theinstructions also cause the processing circuitry to perform an inversetransform operation on the at least one portion of the frequency-domaindata set to generate a second time-domain data set. The instructionsfurther cause the processing circuitry to identify, based on the secondtime-domain data set, a region of interference in the first time-domaindata set.

Aspect 31. The non-transitory computer-readable medium of either of thetwo preceding aspects, wherein the non-transitory computer-readablemedium is part of an automotive radar configured to be installed on anautomobile.

Aspect 32. A system comprising means for performing the method ofaspects 1-24 or any combination thereof.

This disclosure has attributed functionality to processing circuitry1130. Processing circuitry 1130 may include one or more processors.Processing circuitry 1130 may include any combination of integratedcircuitry, discrete logic circuity, analog circuitry, such as one ormore microprocessors, microcontrollers, digital signal processors,application specific integrated circuits, central processing units,field-programmable gate arrays, hardware accelerators, and/or any otherprocessing resources. In some examples, processing circuitry 1130 mayinclude multiple components, such as any combination of the processingresources listed above, as well as other discrete or integrated logiccircuitry, and/or analog circuitry.

The techniques described in this disclosure may also be embodied orencoded in an article of manufacture including a non-transitorycomputer-readable storage medium, such as memory 1140. Examplenon-transitory computer-readable storage media may include random accessmemory (RAM), read-only memory (ROM), programmable ROM, erasableprogrammable ROM, electronically erasable programmable ROM, flashmemory, a solid-state drive, a hard disk, magnetic media, optical media,or any other computer readable storage devices or tangible computerreadable media. The term “non-transitory” may indicate that the storagemedium is not embodied in a carrier wave or a propagated signal. Incertain examples, a non-transitory storage medium may store data thatcan, over time, change (e.g., in RAM or cache).

In this description, the term “couple” may cover connections,communications, or signal paths that enable a functional relationshipconsistent with this description. For example, if device A generates asignal to control device B to perform an action: (a) in a first example,device A is coupled to device B by direct connection; or (b) in a secondexample, device A is coupled to device B through intervening component Cif intervening component C does not alter the functional relationshipbetween device A and device B, such that device B is controlled bydevice A via the control signal generated by device A.

It is understood that the present disclosure provides a number ofexemplary embodiments and that modifications are possible to theseembodiments. Such modifications are expressly within the scope of thisdisclosure. Furthermore, application of these teachings to otherenvironments, applications, and/or purposes is consistent with andcontemplated by the present disclosure.

What is claimed is:
 1. A method comprising: performing, by processingcircuitry, a first transform operation on a first time-domain data setto generate a frequency-domain data set; determining, by the processingcircuitry, that at least one portion of the frequency-domain data setsatisfies a first threshold magnitude; performing, by the processingcircuitry, an inverse transform operation on the at least one portion ofthe frequency-domain data set to generate a second time-domain data set;and identifying, by the processing circuitry and based on the secondtime-domain data set, a region of interference in the first time-domaindata set.
 2. The method of claim 1, further comprising: performing anabsolute-value operation on the second time-domain data set to generatea filtered time-domain data set; and identifying the region ofinterference by at least determining that a portion of the filteredtime-domain data set satisfies a second threshold magnitude.
 3. Themethod of claim 1, further comprising: performing a differencingoperation on the second time-domain data set to generate a differentialtime-domain data set; performing an absolute-value operation on thedifferential time-domain data set to generate a filtered time-domaindata set; and identifying the region of interference by at leastdetermining that a portion of the filtered time-domain data setsatisfies a second threshold magnitude.
 4. The method of claim 1,further comprising: subtracting a portion of the second time-domain dataset from the region of interference in the first time-domain data set togenerate a reconstructed region; and inserting the reconstructed regioninto the first time-domain data set.
 5. The method of claim 1, furthercomprising: subtracting the second time-domain data set from the firsttime-domain data set to generate a third time-domain data set; andperforming a thresholding operation on the third time-domain data set togenerate a filtered time-domain data set, wherein identifying the regionof interference is based on the filtered time-domain data set.
 6. Themethod of claim 1, further comprising: subtracting the secondtime-domain data set from the first time-domain data set to generate athird time-domain data set; identifying a portion of the thirdtime-domain data set corresponding to the region of interference in thefirst time-domain data set; and inserting the portion of the thirdtime-domain data set into the first time-domain data set.
 7. The methodof claim 1, further comprising: determining that a remaining portion ofthe frequency-domain data set does not satisfy the first thresholdmagnitude; performing the inverse transform operation on the remainingportion of the frequency-domain data set to generate a third time-domaindata set; identifying a portion of the third time-domain data setcorresponding to the region of interference in the first time-domaindata set; and inserting the portion of the third time-domain data setinto the first time-domain data set.
 8. A device comprising: a receiverconfigured to generate an analog signal based on received signals; ananalog-to-digital converter configured to convert the analog signal to afirst time-domain data set; and processing circuitry configured to:perform a first transform operation on the first time-domain data set togenerate a frequency-domain data set; determine that at least oneportion of the frequency-domain data set satisfies a first thresholdmagnitude; perform an inverse transform operation on the at least oneportion of the frequency-domain data set to generate a secondtime-domain data set; and identify, based on the second time-domain dataset, a region of interference in the first time-domain data set.
 9. Thedevice of claim 8, wherein the processing circuitry is furtherconfigured to: perform an absolute-value operation on the secondtime-domain data set to generate a filtered time-domain data set; andidentify the region of interference by at least determining that aportion of the filtered time-domain data set satisfies a secondthreshold magnitude.
 10. The device of claim 8, wherein the processingcircuitry is further configured to: perform a differencing operation onthe second time-domain data set to generate a differential time-domaindata set; perform an absolute-value operation on the differentialtime-domain data set to generate a filtered time-domain data set; andidentify the region of interference by at least determining that aportion of the filtered time-domain data set satisfies a secondthreshold magnitude.
 11. The device of claim 8, wherein the processingcircuitry is further configured to: subtract a portion of the secondtime-domain data set from the region of interference in the firsttime-domain data set to generate a reconstructed region; and insert thereconstructed region into the first time-domain data set.
 12. The deviceof claim 8, wherein the processing circuitry is further configured to:subtract the second time-domain data set from the first time-domain dataset to generate a third time-domain data set; and perform a thresholdingoperation on the third time-domain data set to generate a filteredtime-domain data set, wherein the processing circuitry is configured toidentify the region of interference based on the filtered time-domaindata set.
 13. The device of claim 8, wherein the processing circuitry isfurther configured to: subtract the second time-domain data set from thefirst time-domain data set to generate a third time-domain data set;identify a portion of the third time-domain data set corresponding tothe region of interference in the first time-domain data set; and insertthe portion of the third time-domain data set into the first time-domaindata set.
 14. The device of claim 8, wherein the processing circuitry isfurther configured to: determine that a remaining portion of thefrequency-domain data set does not satisfy the first thresholdmagnitude; perform the inverse transform operation on the remainingportion of the frequency-domain data set to generate a third time-domaindata set; identify a portion of the third time-domain data setcorresponding to the region of interference in the first time-domaindata set; and insert the portion of the third time-domain data set intothe first time-domain data set.
 15. A non-transitory computer-readablemedium having executable instructions stored thereon, configured to beexecutable by processing circuitry for causing the processing circuitryto: perform a first transform operation on a first time-domain data setto generate a frequency-domain data set; determine that at least oneportion of the frequency-domain data set satisfies a first thresholdmagnitude; perform an inverse transform operation on the at least oneportion of the frequency-domain data set to generate a secondtime-domain data set; and identify, based on the second time-domain dataset, a region of interference in the first time-domain data set.
 16. Thenon-transitory computer-readable medium of claim 15, wherein theexecutable instructions are configured to be executable by theprocessing circuitry for further causing the processing circuitry to:perform an absolute-value operation on the second time-domain data setto generate a filtered time-domain data set; and identify the region ofinterference by at least determining that a portion of the filteredtime-domain data set satisfies a second threshold magnitude.
 17. Thenon-transitory computer-readable medium of claim 15, wherein theexecutable instructions are configured to be executable by theprocessing circuitry for further causing the processing circuitry to:subtract a portion of the second time-domain data set from the region ofinterference in the first time-domain data set to generate areconstructed region; and insert the reconstructed region into the firsttime-domain data set.
 18. The non-transitory computer-readable medium ofclaim 15, wherein the executable instructions are configured to beexecutable by the processing circuitry for further causing theprocessing circuitry to: subtract the second time-domain data set fromthe first time-domain data set to generate a third time-domain data set;and perform a thresholding operation on the third time-domain data setto generate a filtered time-domain data set, wherein the executableinstructions to identify the region of interference compriseinstructions to identify the region of interference based on thefiltered time-domain data set.
 19. The non-transitory computer-readablemedium of claim 15, wherein the executable instructions are configuredto be executable by the processing circuitry for further causing theprocessing circuitry to: subtract the second time-domain data set fromthe first time-domain data set to generate a third time-domain data set;identify a portion of the third time-domain data set corresponding tothe region of interference in the first time-domain data set; and insertthe portion of the third time-domain data set into the first time-domaindata set.
 20. The non-transitory computer-readable medium of claim 15,wherein the executable instructions are configured to be executable bythe processing circuitry for further causing the processing circuitryto: determine that a remaining portion of the frequency-domain data setdoes not satisfy the first threshold magnitude; perform the inversetransform operation on the remaining portion of the frequency-domaindata set to generate a third time-domain data set; identify a portion ofthe third time-domain data set corresponding to the region ofinterference in the first time-domain data set; and insert the portionof the third time-domain data set into the first time-domain data set.